Data sorting and pulse sorting in particular are often integral to the real-time tracking of radar emitters. Modernly, a typical radar tracking system includes a receiver system and a digital processing system. The receiver system is typically having of an antenna, or antenna elements themselves having antenna array, a multi-channel receiver, signal down-conversion, and some analog processing. The digital processing system is typically includes of dedicated, high-speed hardware processing and software/CPU-based processing.
In order to sort, associate or reject each signal from the myriad of signals that a sensitive radar tracking system intercepts, each instantaneous signal intercepted by the receiver system is typically characterized by a set of parameters prior to storage and processing. This characterization provides the information required to associate a set of signals belonging to a particular emitter and to uniquely identify the particular emitter from among the several emitters whose signals may have been intercepted. The parameters generally measured by the receiver system for a pulsed signal include carrier frequency or radio frequency (RF), pulse amplitude (PA), pulsewidth (PW), time-of-arrival (TOA), and angle-of-arrival (AOA). Also, in some systems, polarization of the input signal is measured. Frequency modulation on-the-pulse (FMOP) is another parameter that can be used to identify a particular emitter and also can be used to determine chirp rate of the phase coding of a signal using pulse compression. Continuous wave (CW) signals are generally identified as those signals whose pulse lengths exceed several hundred microseconds.
TOA measures are made with respect to an internal clock at the leading edge of the pulse. AOA measures can be enhanced or replaced by AOA determination processes typically calculated in the software digital processing. With interferometric devices, it is typical that the amplitude and phase difference for each channel, receiver temperature and instantaneous frequency of every digital sampling point of a valid pulse be both designated by a unique pulse number and recorded. The parameters measured on a single intercepted pulse are typically stored in a data vector called a pulse descriptor word (PDW) or a “data group.” Multiple PDWs form a set of vectors in parameter space. By matching vectors from multiple pulses, it is possible to isolate those signals associated with a particular emitter. This process of association and isolation of signals is called deinterleaving.
Deinterleaving can be accomplished through pulse-by-pulse processing techniques relying on the matching of a number of pulse characteristics (e.g., RF, AOA and TOA) and can benefit greatly from histogram pre-processing approaches. Thereafter, pulse repetition intervals (PRI) and other derived parameters can be computed for enhanced emitter characterization.
The pulse-by-pulse deinterleaving of pulse trains can be significantly complicated by missing or corrupted PDWs leading to an increase in false emitter detections. Histogram processing approaches are far less sensitive to missed or corrupted pulse measurements and are often used as pre-sorters for conventional pulse-by-pulse processing techniques. RF/AOA cells can be used in a histogram processing approach where, upon achieving a predetermined number of PDWs, the contents of the cell are processed using conventional pulse-by-pulse processing approaches. While RF/AOA histogram processing is typically of low resolution, there are efforts to extend the histogramming approach to RF and time-difference-of-arrival (TDOA) organized data. U.S. Pat. No. 5,063,385 issued to J. Caschera addresses the memory intensive nature of this extended histogramming approach.
Deinterleaving can be computationally burdensome. The prior art recognizes that in order to relieve some of this burden, one must relocate some of the sorting functions to upstream hardware devices. U.S. Pat. No. 5,704,057 issued to K. Cho attempts to relieve some of this computational burden with a sorted addressable memory that associates TOA with AOA or other relevant parameters such as the RF. Unfortunately, the approach inherently lacks an economy of scale because the implementation scales linearly with the parameters to be associated with TOA. In addition, there is no interrelation between the sorted parameters.
The several embodiments of the present invention address a pressing need to deinterleave detected emissions with computationally efficient histogramming techniques as part of the signal pre-processing that may be followed by pulse-by-pulse processing.
The several embodiments of the present invention produce pre-sorted data with electronics relying on minimal aid from extrinsic processing resources. In addition, the several embodiments of the present invention provide pre-sorted data that permits the processor to selectively retrieve small portions of the pulse data that are most likely to correspond to a particular emitter within a multi-emitter environment. The several embodiments of the present invention satisfy the pressing need through efficient data grouping and a bifurcation of the data processing along with the partitioning of the main memory generally (and the RAM particularly) into two or more frequency bins to form a frequency histogram.